The field of the present invention relates generally to the coding of speech in voice communication systems and, more particularly to an improved code-excited linear prediction coding system and method of coding the gain quantization parameters of a speech signal with fewer bits.
To model basic speech sounds, speech signals are sampled over time and stored in frames as a discrete waveform to be digitally processed. However, in order to increase the efficient use of the communication bandwidth for speech, speech is coded before being transmitted especially when speech is intended to be transmitted under limited bandwidth constraints. Numerous algorithms have been proposed for the various aspects of speech coding. In coding speech, the speech coding algorithm tries to represent characteristics of the speech signal in a manner which requires less bandwidth. For example, the speech coding algorithm seeks to remove redundancies in the speech signal. A first step is to remove short-term correlations. One type of signal coding technique is linear predictive coding (LPC). In using a LPC approach, the speech signal value at any particular time is modeled as a linear function of previous values. By using a LPC approach, short-term correlations can be reduced and efficient speech signal representations can be determined by estimating and applying certain prediction parameters to represent the signal. After the removal of short-term correlations in a speech signal, a LPC residual signal remains. This residual signal contains periodicity information that needs to be modeled. The second step in removing redundancies in speech is to model the periodicity information. Periodicity information may be modeled by using pitch prediction. Certain portions of speech have periodicity while other portions do not. For example, the sound “aah” has periodicity information while the sound “shhh” has no periodicity information.
In applying the LPC technique, a conventional source encoder operates on speech signals to extract modeling and parameter information to be coded for communication to a conventional source decoder via a communication channel. One way to code modeling and parameter information into a smaller amount of information is to use quantization. Quantization of a parameter involves selecting the closest entry in a table or codebook to represent the parameter. Thus, for example, a parameter of 0.125 may be represented by 0.1 if the codebook contains 0, 0.1, 0.2, 0.3, etc. Quantization includes scalar quantization and vector quantization. In scalar quantization, one selects the entry in the table or codebook that is the closest approximation to the parameter, as described above. By contrast, vector quantization combines two or more parameters and selects the entry in the table or codebook which is closest to the combined parameters. For example, vector quantization may select the entry in the codebook that is the closest to the difference between the parameters. A codebook used to vector quantize two parameters at once is often referred to as a two-dimensional codebook. A n-dimensional codebook quantizes n parameters at once.
In CELP (Code Excited Linear Prediction) speech coding, there are two types of gain. The first type of gain is the pitch gain GP, also known as the adaptive codebook gain. The adaptive codebook gain is sometimes referred to, including herein, with the subscript “a” instead of the subscript “p”. The second type of gain is the fixed codebook gain GC. Speech coding algorithms have quantized parameters including the adaptive codebook gain and the fixed codebook gain. Once coded, the parameters representing the input speech signal are transmitted to a transceiver.
At the transceiver, a decoder receives the coded information. Because the decoder is configured to know the manner in which speech signals are encoded, the decoder decodes the coded information to reconstruct a signal for playback that sounds to the human ear like the original speech.
Therefore, transmitting the coded modeling and parameter information to the decoder requires a certain amount of valuable communication channel bandwidth. In order to increase the efficient use of the bandwidth, improvements to the manner in which modeling and parameter information is coded are needed. Coding algorithms need to reduce the amount of information in bits that must be transmitted over the communication channel. However, there is a countervailing need for a coding algorithm that not only reduces the amount of information in bits that must be communicated over the channel, but also maintains a high quality level of the reproduced speech.